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Record W1993694718 · doi:10.1002/pc.20755

Experimental characterization and numerical simulation of the humidity absorption process in glass reinforced composites under dissymmetric exposure conditions

2009· article· en· W1993694718 on OpenAlexaff
Hachmi Ben Daly, Manel Harchay, Hédi Belhadjsalah, Rachid Boukhili

Bibliographic record

VenuePolymer Composites · 2009
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMaterials scienceComposite materialImmersion (mathematics)Absorption of waterThermal diffusivityGravimetric analysisMoistureFick's laws of diffusionGlass fiberVoid (composites)HumidityDiffusionWater contentThermodynamics

Abstract

fetched live from OpenAlex

Abstract This article investigates the dissymmetrical water absorption process in E‐glass fiber reinforced polyester composites containing fillers and low profile additives. Two different cases of dissymmetrical exposures to water were considered. In the first case, only one side of the aged specimen was exposed to water at different temperatures, the other side was exposed to air. In the second case, both sides were exposed to water, but at different temperatures on each side. The moisture diffusivity and the maximum moisture content reached by the composites were determined using the gravimetric test method. In the first case of the dissymmetrical immersion, the temperature inside the aged specimen was found to be almost constant and the kinetic of the water diffusion was found to obey perfectly the one‐dimensional Fick's second law. In the second case, the specimen temperature varies from one side to the other, thus preventing the use of Fick's laws. Alternatively, the use of the finite element code ABAQUS provided an excellent agreement between the experimental and simulated results in both cases of dissymmetrical immersions, as well as in the case of symmetrical immersion where the two sides were exposed to the same environmental conditions. POLYM. COMPOS., 2009. © 2009 Society of Plastics Engineers

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.262
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2009
Admission routes1
Has abstractyes

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